import pandas as pd
import matplotlib.pyplot as plt
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
from sklearn.model_selection import cross_val_score, cross_validate
from sklearn.metrics import accuracy_score
from sklearn.metrics import precision_score
from sklearn.metrics import recall_score
from sklearn.metrics import f1_score
from sklearn.metrics import plot_confusion_matrix
from sklearn.metrics import balanced_accuracy_score
from sklearn.metrics import confusion_matrix
from xgboost import XGBClassifier
from sklearn.model_selection import GridSearchCV

def xgb_model_init(X_train,y_train):
    xgb_clf = XGBClassifier(objective='binary:logistic', eval_metric='auc')
    xgb_clf.fit(X_train,y_train)
    
    return xgb_clf

def xgb_model_tuned(X_train,y_train):
    
    grid_search = {'silent': [True],
               'max_depth': [5,8,10],
               'min_child_weight': [4],
               'learning_rate': [0.1,0.003,0.001],
               'n_estimators': [50,100]}
    clf = XGBClassifier()
    grid = GridSearchCV(estimator = clf, param_grid = grid_search, 
                               cv = 4, verbose= 5, n_jobs = -1)
    grid.fit(X_train,y_train)
    
    xgb_model=grid.best_estimator_
    return xgb_model

data = pd.read_csv('preprocessing1.csv', encoding = 'cp949', index_col=0 )
seed = 5764

target=data['Status']
data.drop(['Status'],axis=1,inplace = True)

# 학습, 테스트 데이터 분리 (0.7:0.3)
x_train, x_test, y_train, y_test = train_test_split(data, target, test_size=0.3, random_state=seed)

xgb_model = xgb_model_tuned(x_train,y_train)

Fitting 4 folds for each of 18 candidates, totalling 72 fits
[22:20:52] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.6.0/src/learner.cc:627: 
Parameters: { "silent" } might not be used.

  This could be a false alarm, with some parameters getting used by language bindings but
  then being mistakenly passed down to XGBoost core, or some parameter actually being used
  but getting flagged wrongly here. Please open an issue if you find any such cases.

import numpy as np
from lime import lime_tabular

xgb_lime_explainer = lime_tabular.LimeTabularExplainer(np.array(x_train),feature_names = data.columns , mode="classification")
for i in range(0,10):
    xgb_lime_explanation = xgb_lime_explainer.explain_instance(x_test.iloc[i], xgb_model.predict_proba)
    xgb_lime_explanation.show_in_notebook(show_table=True)
    print(i)

0

1

2

3

4

5

6

7

8

9

import shap 

xgb_shap_explainer = shap.Explainer(xgb_model)
shap_values = xgb_shap_explainer.shap_values(x_test)

ntree_limit is deprecated, use `iteration_range` or model slicing instead.

shap.summary_plot(shap_values,x_train,plot_size = 1.0)

shap.dependence_plot('rate_of_interest', shap_values, x_test)

shap.summary_plot(shap_values, x_test, plot_type='bar')